Overview

Dataset statistics

Number of variables20
Number of observations28200
Missing cells22614
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 MiB
Average record size in memory262.7 B

Variable types

Numeric18
Categorical2

Alerts

area_temperature(°C) is highly overall correlated with generator_temperature(°C) and 1 other fieldsHigh correlation
engine_temperature(°C) is highly overall correlated with windmill_generated_power(kW/h)High correlation
generator_temperature(°C) is highly overall correlated with area_temperature(°C) and 5 other fieldsHigh correlation
motor_torque(N-m) is highly overall correlated with area_temperature(°C) and 5 other fieldsHigh correlation
resistance(ohm) is highly overall correlated with generator_temperature(°C) and 2 other fieldsHigh correlation
rotor_torque(N-m) is highly overall correlated with generator_temperature(°C) and 2 other fieldsHigh correlation
wind_speed(m/s) is highly overall correlated with generator_temperature(°C) and 1 other fieldsHigh correlation
windmill_generated_power(kW/h) is highly overall correlated with engine_temperature(°C) and 2 other fieldsHigh correlation
atmospheric_temperature(°C) has 3450 (12.2%) missing valuesMissing
atmospheric_pressure(Pascal) has 2707 (9.6%) missing valuesMissing
windmill_body_temperature(°C) has 2363 (8.4%) missing valuesMissing
wind_direction(°) has 5103 (18.1%) missing valuesMissing
rotor_torque(N-m) has 572 (2.0%) missing valuesMissing
turbine_status has 1759 (6.2%) missing valuesMissing
blade_length(m) has 5093 (18.1%) missing valuesMissing
windmill_height(m) has 543 (1.9%) missing valuesMissing
blade_breadth(m) has unique valuesUnique

Reproduction

Analysis started2024-02-20 23:42:13.278130
Analysis finished2024-02-20 23:43:58.864117
Duration1 minute and 45.59 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

wind_speed(m/s)
Real number (ℝ)

HIGH CORRELATION 

Distinct27727
Distinct (%)99.3%
Missing273
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean69.037071
Minimum-496.21103
Maximum601.45567
Zeros0
Zeros (%)0.0%
Negative2142
Negative (%)7.6%
Memory size220.4 KiB
2024-02-20T23:43:59.043927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-496.21103
5-th percentile-65.754867
Q120.883502
median93.302129
Q395.268058
95-th percentile212.07453
Maximum601.45567
Range1097.6667
Interquartile range (IQR)74.384556

Descriptive statistics

Standard deviation76.275645
Coefficient of variation (CV)1.1048505
Kurtosis4.1125999
Mean69.037071
Median Absolute Deviation (MAD)3.5521794
Skewness-0.060477165
Sum1927998.3
Variance5817.974
MonotonicityNot monotonic
2024-02-20T23:43:59.355323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 193
 
0.7%
81.81818182 9
 
< 0.1%
94.82002337 1
 
< 0.1%
243.9489463 1
 
< 0.1%
-25.00411662 1
 
< 0.1%
91.43088188 1
 
< 0.1%
14.46138604 1
 
< 0.1%
21.30176724 1
 
< 0.1%
94.43363262 1
 
< 0.1%
10.70767818 1
 
< 0.1%
Other values (27717) 27717
98.3%
(Missing) 273
 
1.0%
ValueCountFrequency (%)
-496.2110289 1
< 0.1%
-402.6087362 1
< 0.1%
-391.2085304 1
< 0.1%
-354.0064613 1
< 0.1%
-330.5831315 1
< 0.1%
-329.4538495 1
< 0.1%
-328.4165588 1
< 0.1%
-323.4304627 1
< 0.1%
-321.4299907 1
< 0.1%
-318.0810938 1
< 0.1%
ValueCountFrequency (%)
601.4556704 1
< 0.1%
513.078624 1
< 0.1%
498.8775284 1
< 0.1%
488.0959645 1
< 0.1%
484.6564629 1
< 0.1%
481.8032152 1
< 0.1%
480.2610467 1
< 0.1%
479.5756112 1
< 0.1%
479.2811084 1
< 0.1%
473.7379151 1
< 0.1%

atmospheric_temperature(°C)
Real number (ℝ)

MISSING 

Distinct20809
Distinct (%)84.1%
Missing3450
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean0.38372707
Minimum-99
Maximum80.217444
Zeros0
Zeros (%)0.0%
Negative4199
Negative (%)14.9%
Memory size220.4 KiB
2024-02-20T23:43:59.642576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q17.9489001
median16.10241
Q323.687285
95-th percentile35.771145
Maximum80.217444
Range179.21744
Interquartile range (IQR)15.738385

Descriptive statistics

Standard deviation44.278534
Coefficient of variation (CV)115.3907
Kurtosis1.1376025
Mean0.38372707
Median Absolute Deviation (MAD)7.8561585
Skewness-1.6748952
Sum9497.245
Variance1960.5885
MonotonicityNot monotonic
2024-02-20T23:43:59.932138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99 3942
 
14.0%
20.35047022 1
 
< 0.1%
29.53471562 1
 
< 0.1%
12.8364429 1
 
< 0.1%
33.88303923 1
 
< 0.1%
13.18569888 1
 
< 0.1%
21.7014444 1
 
< 0.1%
4.693476334 1
 
< 0.1%
-8.583088807 1
 
< 0.1%
11.79135651 1
 
< 0.1%
Other values (20799) 20799
73.8%
(Missing) 3450
 
12.2%
ValueCountFrequency (%)
-99 3942
14.0%
-53.79581308 1
 
< 0.1%
-33.11377025 1
 
< 0.1%
-28.44556748 1
 
< 0.1%
-27.48416876 1
 
< 0.1%
-26.62347465 1
 
< 0.1%
-26.22129642 1
 
< 0.1%
-25.91006798 1
 
< 0.1%
-25.8286267 1
 
< 0.1%
-24.84069673 1
 
< 0.1%
ValueCountFrequency (%)
80.21744352 1
< 0.1%
76.90265697 1
< 0.1%
74.76536332 1
< 0.1%
72.44945085 1
< 0.1%
72.1202695 1
< 0.1%
72.0326493 1
< 0.1%
69.63372796 1
< 0.1%
69.31933899 1
< 0.1%
68.94892575 1
< 0.1%
68.78096735 1
< 0.1%

shaft_temperature(°C)
Real number (ℝ)

Distinct27625
Distinct (%)98.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40.085387
Minimum-99
Maximum169.82046
Zeros0
Zeros (%)0.0%
Negative1417
Negative (%)5.0%
Memory size220.4 KiB
2024-02-20T23:44:00.243470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-0.16215364
Q141.633238
median43.686082
Q345.673685
95-th percentile77.860796
Maximum169.82046
Range268.82046
Interquartile range (IQR)4.0404478

Descriptive statistics

Standard deviation27.20443
Coefficient of variation (CV)0.67866202
Kurtosis12.246622
Mean40.085387
Median Absolute Deviation (MAD)2.0197266
Skewness-2.5251683
Sum1130327.7
Variance740.08099
MonotonicityNot monotonic
2024-02-20T23:44:00.543569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99 557
 
2.0%
30 10
 
< 0.1%
-5 9
 
< 0.1%
42.62213449 1
 
< 0.1%
42.0019404 1
 
< 0.1%
42.70678089 1
 
< 0.1%
79.39202925 1
 
< 0.1%
42.68878809 1
 
< 0.1%
46.50431983 1
 
< 0.1%
41.67928037 1
 
< 0.1%
Other values (27615) 27615
97.9%
(Missing) 2
 
< 0.1%
ValueCountFrequency (%)
-99 557
2.0%
-93.08623556 1
 
< 0.1%
-76.26809109 1
 
< 0.1%
-75.8702313 1
 
< 0.1%
-73.71328469 1
 
< 0.1%
-69.17141482 1
 
< 0.1%
-67.18625304 1
 
< 0.1%
-65.82069531 1
 
< 0.1%
-65.03887445 1
 
< 0.1%
-64.17271023 1
 
< 0.1%
ValueCountFrequency (%)
169.8204551 1
< 0.1%
166.4406054 1
< 0.1%
155.3044514 1
< 0.1%
151.0166952 1
< 0.1%
149.6911781 1
< 0.1%
144.2345366 1
< 0.1%
143.4832318 1
< 0.1%
142.8318294 1
< 0.1%
142.423809 1
< 0.1%
139.3736624 1
< 0.1%

blades_angle(°)
Real number (ℝ)

Distinct22830
Distinct (%)81.6%
Missing216
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean-9.654038
Minimum-146.25954
Maximum165.93212
Zeros5
Zeros (%)< 0.1%
Negative15475
Negative (%)54.9%
Memory size220.4 KiB
2024-02-20T23:44:00.814521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-146.25954
5-th percentile-99
Q1-1.1976516
median-0.4956079
Q35.4950305
95-th percentile65.984351
Maximum165.93212
Range312.19167
Interquartile range (IQR)6.6926821

Descriptive statistics

Standard deviation47.918161
Coefficient of variation (CV)-4.9635356
Kurtosis0.29879648
Mean-9.654038
Median Absolute Deviation (MAD)2.3880847
Skewness-0.65212219
Sum-270158.6
Variance2296.1502
MonotonicityNot monotonic
2024-02-20T23:44:01.105376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99 5151
 
18.3%
0 5
 
< 0.1%
-0.903422903 1
 
< 0.1%
-1.159744995 1
 
< 0.1%
-0.8612459837 1
 
< 0.1%
-0.812420201 1
 
< 0.1%
5.987817458 1
 
< 0.1%
-0.9160369329 1
 
< 0.1%
-1.255432792 1
 
< 0.1%
0.7511123508 1
 
< 0.1%
Other values (22820) 22820
80.9%
(Missing) 216
 
0.8%
ValueCountFrequency (%)
-146.2595427 1
 
< 0.1%
-137.6590444 1
 
< 0.1%
-99 5151
18.3%
-74.7897359 1
 
< 0.1%
-74.70826034 1
 
< 0.1%
-74.40534109 1
 
< 0.1%
-74.38049552 1
 
< 0.1%
-74.34272372 1
 
< 0.1%
-74.31146917 1
 
< 0.1%
-74.28881705 1
 
< 0.1%
ValueCountFrequency (%)
165.9321232 1
< 0.1%
165.1693672 1
< 0.1%
164.5191854 1
< 0.1%
162.2045128 1
< 0.1%
161.7178401 1
< 0.1%
160.9599566 1
< 0.1%
157.5835564 1
< 0.1%
147.8461528 1
< 0.1%
143.1674451 1
< 0.1%
141.8923603 1
< 0.1%
Distinct27911
Distinct (%)99.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean41.027755
Minimum-244.9741
Maximum999
Zeros0
Zeros (%)0.0%
Negative3091
Negative (%)11.0%
Memory size220.4 KiB
2024-02-20T23:44:01.418530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-244.9741
5-th percentile-40.966971
Q140.557952
median43.221735
Q345.879425
95-th percentile117.70774
Maximum999
Range1243.9741
Interquartile range (IQR)5.3214729

Descriptive statistics

Standard deviation43.663605
Coefficient of variation (CV)1.0642455
Kurtosis28.451158
Mean41.027755
Median Absolute Deviation (MAD)2.6618899
Skewness0.88684644
Sum1156941.7
Variance1906.5104
MonotonicityNot monotonic
2024-02-20T23:44:01.719042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99 272
 
1.0%
-5 9
 
< 0.1%
30 8
 
< 0.1%
999 3
 
< 0.1%
82.41057319 1
 
< 0.1%
-22.6128015 1
 
< 0.1%
37.64600065 1
 
< 0.1%
39.78995166 1
 
< 0.1%
-63.921877 1
 
< 0.1%
46.46167943 1
 
< 0.1%
Other values (27901) 27901
98.9%
ValueCountFrequency (%)
-244.9740978 1
< 0.1%
-201.165465 1
< 0.1%
-194.6379341 1
< 0.1%
-189.42395 1
< 0.1%
-188.7634726 1
< 0.1%
-188.4827225 1
< 0.1%
-186.8965665 1
< 0.1%
-186.0517657 1
< 0.1%
-184.3831767 1
< 0.1%
-179.1822148 1
< 0.1%
ValueCountFrequency (%)
999 3
< 0.1%
348.6865421 1
 
< 0.1%
286.0193338 1
 
< 0.1%
276.2525336 1
 
< 0.1%
274.3128291 1
 
< 0.1%
273.2017549 1
 
< 0.1%
272.8810447 1
 
< 0.1%
272.2557484 1
 
< 0.1%
271.166443 1
 
< 0.1%
271.0102641 1
 
< 0.1%

engine_temperature(°C)
Real number (ℝ)

HIGH CORRELATION 

Distinct28188
Distinct (%)100.0%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean42.614239
Minimum3.167151
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size220.4 KiB
2024-02-20T23:44:02.017773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.167151
5-th percentile39.067651
Q141.911365
median43.525297
Q345.174246
95-th percentile47.683844
Maximum50
Range46.832849
Interquartile range (IQR)3.262881

Descriptive statistics

Standard deviation6.1245458
Coefficient of variation (CV)0.14372064
Kurtosis16.766418
Mean42.614239
Median Absolute Deviation (MAD)1.6302601
Skewness-3.9447756
Sum1201210.2
Variance37.510061
MonotonicityNot monotonic
2024-02-20T23:44:02.321914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.52301542 1
 
< 0.1%
41.02053202 1
 
< 0.1%
42.34510499 1
 
< 0.1%
46.32425584 1
 
< 0.1%
42.42983123 1
 
< 0.1%
37.45495648 1
 
< 0.1%
42.31486268 1
 
< 0.1%
12.92453988 1
 
< 0.1%
41.39597642 1
 
< 0.1%
47.75526563 1
 
< 0.1%
Other values (28178) 28178
99.9%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
3.167151021 1
< 0.1%
3.282993896 1
< 0.1%
3.472512809 1
< 0.1%
3.537431034 1
< 0.1%
3.568055439 1
< 0.1%
3.778911285 1
< 0.1%
3.791511509 1
< 0.1%
4.105050044 1
< 0.1%
4.138098316 1
< 0.1%
4.203234849 1
< 0.1%
ValueCountFrequency (%)
50 1
< 0.1%
49.94570021 1
< 0.1%
49.84429924 1
< 0.1%
49.83606572 1
< 0.1%
49.82679657 1
< 0.1%
49.81755142 1
< 0.1%
49.81581512 1
< 0.1%
49.81247126 1
< 0.1%
49.74144995 1
< 0.1%
49.73921689 1
< 0.1%

motor_torque(N-m)
Real number (ℝ)

HIGH CORRELATION 

Distinct27660
Distinct (%)98.2%
Missing24
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1710.8198
Minimum500
Maximum3000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size220.4 KiB
2024-02-20T23:44:02.601184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile617.55414
Q1870.34024
median2031.8495
Q32462.5857
95-th percentile2886.183
Maximum3000
Range2500
Interquartile range (IQR)1592.2455

Descriptive statistics

Standard deviation827.20554
Coefficient of variation (CV)0.48351412
Kurtosis-1.6129816
Mean1710.8198
Median Absolute Deviation (MAD)843.23152
Skewness0.034257515
Sum48204059
Variance684269
MonotonicityNot monotonic
2024-02-20T23:44:03.557370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 306
 
1.1%
1001 212
 
0.8%
2563.124522 1
 
< 0.1%
2171.537665 1
 
< 0.1%
949.2438815 1
 
< 0.1%
2188.531276 1
 
< 0.1%
2136.936132 1
 
< 0.1%
780.2016844 1
 
< 0.1%
828.0163288 1
 
< 0.1%
1932.48774 1
 
< 0.1%
Other values (27650) 27650
98.0%
(Missing) 24
 
0.1%
ValueCountFrequency (%)
500 306
1.1%
500.0908101 1
 
< 0.1%
500.10939 1
 
< 0.1%
500.1236527 1
 
< 0.1%
500.1259891 1
 
< 0.1%
500.1324533 1
 
< 0.1%
500.1547937 1
 
< 0.1%
500.1640179 1
 
< 0.1%
500.175744 1
 
< 0.1%
500.1823596 1
 
< 0.1%
ValueCountFrequency (%)
3000 1
< 0.1%
2992.737332 1
< 0.1%
2992.384328 1
< 0.1%
2990.538307 1
< 0.1%
2989.188415 1
< 0.1%
2987.745846 1
< 0.1%
2987.727252 1
< 0.1%
2987.58897 1
< 0.1%
2987.126798 1
< 0.1%
2985.664017 1
< 0.1%

generator_temperature(°C)
Real number (ℝ)

HIGH CORRELATION 

Distinct28187
Distinct (%)> 99.9%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean65.027857
Minimum33.893779
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size220.4 KiB
2024-02-20T23:44:03.842915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum33.893779
5-th percentile37.40262
Q141.198509
median70.729533
Q378.945849
95-th percentile93.918057
Maximum100
Range66.106221
Interquartile range (IQR)37.747339

Descriptive statistics

Standard deviation19.816499
Coefficient of variation (CV)0.30473862
Kurtosis-1.3699911
Mean65.027857
Median Absolute Deviation (MAD)17.263401
Skewness-0.1908094
Sum1833005.2
Variance392.69365
MonotonicityNot monotonic
2024-02-20T23:44:04.136050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 2
 
< 0.1%
76.66555995 1
 
< 0.1%
75.73951279 1
 
< 0.1%
72.73509868 1
 
< 0.1%
38.03858464 1
 
< 0.1%
45.12072279 1
 
< 0.1%
66.83550214 1
 
< 0.1%
36.28181015 1
 
< 0.1%
69.88468343 1
 
< 0.1%
39.17167508 1
 
< 0.1%
Other values (28177) 28177
99.9%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
33.89377879 1
< 0.1%
33.92289925 1
< 0.1%
33.92494355 1
< 0.1%
33.92795489 1
< 0.1%
33.93688909 1
< 0.1%
33.9462632 1
< 0.1%
33.95281803 1
< 0.1%
33.95961 1
< 0.1%
34.00620617 1
< 0.1%
34.01213645 1
< 0.1%
ValueCountFrequency (%)
100 1
< 0.1%
99.73494076 1
< 0.1%
99.52080602 1
< 0.1%
99.51450453 1
< 0.1%
99.33659699 1
< 0.1%
99.22236155 1
< 0.1%
99.17120051 1
< 0.1%
99.10044506 1
< 0.1%
99.05374751 1
< 0.1%
99.02963663 1
< 0.1%

atmospheric_pressure(Pascal)
Real number (ℝ)

MISSING 

Distinct25492
Distinct (%)> 99.9%
Missing2707
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean53185.065
Minimum-1188624.1
Maximum1272551.9
Zeros0
Zeros (%)0.0%
Negative3282
Negative (%)11.6%
Memory size220.4 KiB
2024-02-20T23:44:04.462267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1188624.1
5-th percentile-291773.96
Q116794.921
median18191.126
Q3118113.29
95-th percentile396465.3
Maximum1272551.9
Range2461176
Interquartile range (IQR)101318.37

Descriptive statistics

Standard deviation187503.62
Coefficient of variation (CV)3.5254938
Kurtosis4.4798775
Mean53185.065
Median Absolute Deviation (MAD)89036.049
Skewness0.056670932
Sum1.3558469 × 109
Variance3.5157606 × 1010
MonotonicityNot monotonic
2024-02-20T23:44:04.759900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15000 2
 
< 0.1%
103402.9619 1
 
< 0.1%
-15201.82889 1
 
< 0.1%
17801.9587 1
 
< 0.1%
18598.22275 1
 
< 0.1%
17661.34085 1
 
< 0.1%
17715.11794 1
 
< 0.1%
114294.7062 1
 
< 0.1%
16924.00384 1
 
< 0.1%
421233.6353 1
 
< 0.1%
Other values (25482) 25482
90.4%
(Missing) 2707
 
9.6%
ValueCountFrequency (%)
-1188624.131 1
< 0.1%
-1021641.599 1
< 0.1%
-1019257.164 1
< 0.1%
-1010616.308 1
< 0.1%
-1010037.08 1
< 0.1%
-962090.5599 1
< 0.1%
-952409.5623 1
< 0.1%
-925996.2062 1
< 0.1%
-916586.0077 1
< 0.1%
-914153.8817 1
< 0.1%
ValueCountFrequency (%)
1272551.895 1
< 0.1%
1265097.692 1
< 0.1%
1205423.99 1
< 0.1%
1187983.533 1
< 0.1%
1182842.732 1
< 0.1%
1135375.716 1
< 0.1%
1123587.911 1
< 0.1%
1114198.172 1
< 0.1%
1105042.399 1
< 0.1%
1093598.165 1
< 0.1%

area_temperature(°C)
Real number (ℝ)

HIGH CORRELATION 

Distinct28170
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.735091
Minimum-30
Maximum55
Zeros0
Zeros (%)0.0%
Negative39
Negative (%)0.1%
Memory size220.4 KiB
2024-02-20T23:44:05.057659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-30
5-th percentile21.534225
Q127.311644
median32.605195
Q338.232387
95-th percentile45.08603
Maximum55
Range85
Interquartile range (IQR)10.920743

Descriptive statistics

Standard deviation7.703391
Coefficient of variation (CV)0.23532517
Kurtosis4.4365145
Mean32.735091
Median Absolute Deviation (MAD)5.4491181
Skewness-0.62542431
Sum923129.57
Variance59.342232
MonotonicityNot monotonic
2024-02-20T23:44:05.353632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-30 31
 
0.1%
24.43302079 1
 
< 0.1%
23.98480247 1
 
< 0.1%
29.45312356 1
 
< 0.1%
33.3997364 1
 
< 0.1%
18.00533959 1
 
< 0.1%
37.35759271 1
 
< 0.1%
28.04335934 1
 
< 0.1%
29.6311022 1
 
< 0.1%
37.4966505 1
 
< 0.1%
Other values (28160) 28160
99.9%
ValueCountFrequency (%)
-30 31
0.1%
-24.96294909 1
 
< 0.1%
-20.77581285 1
 
< 0.1%
-16.11030544 1
 
< 0.1%
-7.473661188 1
 
< 0.1%
-6.858997714 1
 
< 0.1%
-3.754995435 1
 
< 0.1%
-0.5795874579 1
 
< 0.1%
-0.4258384779 1
 
< 0.1%
0.4197413128 1
 
< 0.1%
ValueCountFrequency (%)
55 1
< 0.1%
54.85312721 1
< 0.1%
54.70928563 1
< 0.1%
54.10713965 1
< 0.1%
53.90802656 1
< 0.1%
53.64172732 1
< 0.1%
53.27180696 1
< 0.1%
53.23789796 1
< 0.1%
53.16332252 1
< 0.1%
53.12329824 1
< 0.1%

windmill_body_temperature(°C)
Real number (ℝ)

MISSING 

Distinct21893
Distinct (%)84.7%
Missing2363
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean20.799761
Minimum-999
Maximum323
Zeros0
Zeros (%)0.0%
Negative4662
Negative (%)16.5%
Memory size220.4 KiB
2024-02-20T23:44:05.622936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-99
Q140.448386
median42.786832
Q344.494543
95-th percentile48.457194
Maximum323
Range1322
Interquartile range (IQR)4.0461574

Descriptive statistics

Standard deviation54.356431
Coefficient of variation (CV)2.61332
Kurtosis15.197601
Mean20.799761
Median Absolute Deviation (MAD)1.9054312
Skewness-2.2368322
Sum537403.41
Variance2954.6216
MonotonicityNot monotonic
2024-02-20T23:44:05.951081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99 3926
 
13.9%
30 12
 
< 0.1%
-5 7
 
< 0.1%
-999 3
 
< 0.1%
43.63291406 1
 
< 0.1%
44.52033035 1
 
< 0.1%
44.14973697 1
 
< 0.1%
42.77546037 1
 
< 0.1%
42.51396958 1
 
< 0.1%
43.28569967 1
 
< 0.1%
Other values (21883) 21883
77.6%
(Missing) 2363
 
8.4%
ValueCountFrequency (%)
-999 3
 
< 0.1%
-100.2688756 1
 
< 0.1%
-99 3926
13.9%
-72.26550484 1
 
< 0.1%
-70.80879441 1
 
< 0.1%
-68.95221428 1
 
< 0.1%
-67.83613011 1
 
< 0.1%
-67.63420811 1
 
< 0.1%
-67.54871309 1
 
< 0.1%
-64.73431588 1
 
< 0.1%
ValueCountFrequency (%)
323 1
< 0.1%
160.293271 1
< 0.1%
158.2237161 1
< 0.1%
152.606752 1
< 0.1%
151.4673752 1
< 0.1%
151.1724419 1
< 0.1%
145.1459787 1
< 0.1%
143.0183029 1
< 0.1%
142.1149662 1
< 0.1%
141.0579566 1
< 0.1%

wind_direction(°)
Real number (ℝ)

MISSING 

Distinct22984
Distinct (%)99.5%
Missing5103
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean306.88888
Minimum0
Maximum569.96648
Zeros114
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size220.4 KiB
2024-02-20T23:44:06.246560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54.310545
Q1238.62775
median271.42766
Q3404.15352
95-th percentile532.60998
Maximum569.96648
Range569.96648
Interquartile range (IQR)165.52576

Descriptive statistics

Standard deviation134.0559
Coefficient of variation (CV)0.43682228
Kurtosis-0.3182066
Mean306.88888
Median Absolute Deviation (MAD)47.438042
Skewness0.17194161
Sum7088212.5
Variance17970.984
MonotonicityNot monotonic
2024-02-20T23:44:06.610611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 114
 
0.4%
239.8363875 1
 
< 0.1%
213.9976236 1
 
< 0.1%
228.2772607 1
 
< 0.1%
495.3509272 1
 
< 0.1%
258.2104271 1
 
< 0.1%
270.0170937 1
 
< 0.1%
113.2576612 1
 
< 0.1%
203.9612437 1
 
< 0.1%
245.4759826 1
 
< 0.1%
Other values (22974) 22974
81.5%
(Missing) 5103
 
18.1%
ValueCountFrequency (%)
0 114
0.4%
0.782832502 1
 
< 0.1%
0.9111314705 1
 
< 0.1%
1.723958823 1
 
< 0.1%
1.794628479 1
 
< 0.1%
1.805685491 1
 
< 0.1%
1.946548582 1
 
< 0.1%
2.056508582 1
 
< 0.1%
2.140629227 1
 
< 0.1%
2.147377189 1
 
< 0.1%
ValueCountFrequency (%)
569.9664788 1
< 0.1%
569.879236 1
< 0.1%
569.8682998 1
< 0.1%
569.2928645 1
< 0.1%
568.8889251 1
< 0.1%
568.8654776 1
< 0.1%
568.5048979 1
< 0.1%
568.2804165 1
< 0.1%
567.1502374 1
< 0.1%
567.0560563 1
< 0.1%

resistance(ohm)
Real number (ℝ)

HIGH CORRELATION 

Distinct27365
Distinct (%)97.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1575.56
Minimum-1005.223
Maximum4693.4819
Zeros0
Zeros (%)0.0%
Negative616
Negative (%)2.2%
Memory size220.4 KiB
2024-02-20T23:44:07.072488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1005.223
5-th percentile750.35341
Q11268.134
median1678.2384
Q31829.054
95-th percentile1991.9221
Maximum4693.4819
Range5698.7049
Interquartile range (IQR)560.91996

Descriptive statistics

Standard deviation483.32639
Coefficient of variation (CV)0.30676483
Kurtosis3.738643
Mean1575.56
Median Absolute Deviation (MAD)257.00856
Skewness-0.69780865
Sum44429217
Variance233604.4
MonotonicityNot monotonic
2024-02-20T23:44:07.522163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99 558
 
2.0%
1172.554732 198
 
0.7%
1672.554732 81
 
0.3%
2730.310605 1
 
< 0.1%
1428.816911 1
 
< 0.1%
1664.919873 1
 
< 0.1%
1975.543655 1
 
< 0.1%
1207.060625 1
 
< 0.1%
1194.209601 1
 
< 0.1%
1659.024053 1
 
< 0.1%
Other values (27355) 27355
97.0%
ValueCountFrequency (%)
-1005.222988 1
< 0.1%
-825.1568182 1
< 0.1%
-821.5949749 1
< 0.1%
-814.490238 1
< 0.1%
-781.6710998 1
< 0.1%
-763.2128689 1
< 0.1%
-717.9608487 1
< 0.1%
-630.7596794 1
< 0.1%
-630.1418401 1
< 0.1%
-565.8903901 1
< 0.1%
ValueCountFrequency (%)
4693.481933 1
< 0.1%
4389.365064 1
< 0.1%
4327.117453 1
< 0.1%
4304.525934 1
< 0.1%
4295.63407 1
< 0.1%
4187.014584 1
< 0.1%
4150.267159 1
< 0.1%
4131.615761 1
< 0.1%
4120.669249 1
< 0.1%
4050.29201 1
< 0.1%

rotor_torque(N-m)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25945
Distinct (%)93.9%
Missing572
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean25.849894
Minimum-136.73222
Maximum236.88326
Zeros0
Zeros (%)0.0%
Negative2437
Negative (%)8.6%
Memory size220.4 KiB
2024-02-20T23:44:08.042386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-136.73222
5-th percentile-34.515171
Q113.722808
median32.977192
Q341.55052
95-th percentile75.514151
Maximum236.88326
Range373.61548
Interquartile range (IQR)27.827712

Descriptive statistics

Standard deviation32.423943
Coefficient of variation (CV)1.2543163
Kurtosis5.1901709
Mean25.849894
Median Absolute Deviation (MAD)13.618639
Skewness-1.0309474
Sum714180.87
Variance1051.3121
MonotonicityNot monotonic
2024-02-20T23:44:08.542975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99 571
 
2.0%
20 334
 
1.2%
21.14181606 317
 
1.1%
5 242
 
0.9%
5.570908032 224
 
0.8%
82.93435231 1
 
< 0.1%
92.9299745 1
 
< 0.1%
14.3711293 1
 
< 0.1%
19.14698767 1
 
< 0.1%
18.02222967 1
 
< 0.1%
Other values (25935) 25935
92.0%
(Missing) 572
 
2.0%
ValueCountFrequency (%)
-136.7322169 1
< 0.1%
-134.9030992 1
< 0.1%
-134.3422591 1
< 0.1%
-130.7785732 1
< 0.1%
-129.9126308 1
< 0.1%
-127.2231993 1
< 0.1%
-124.5070282 1
< 0.1%
-120.9118882 1
< 0.1%
-120.6007984 1
< 0.1%
-119.2104264 1
< 0.1%
ValueCountFrequency (%)
236.8832642 1
< 0.1%
208.3201757 1
< 0.1%
192.6257984 1
< 0.1%
189.1117899 1
< 0.1%
187.1577121 1
< 0.1%
184.6939501 1
< 0.1%
176.5325279 1
< 0.1%
175.9005218 1
< 0.1%
174.5837645 1
< 0.1%
174.2877434 1
< 0.1%

turbine_status
Categorical

MISSING 

Distinct14
Distinct (%)0.1%
Missing1759
Missing (%)6.2%
Memory size1.6 MiB
BB
1946 
AAA
1939 
BCB
1933 
B2
1931 
A
1930 
Other values (9)
16762 

Length

Max length3
Median length2
Mean length2.0690594
Min length1

Characters and Unicode

Total characters54708
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBA
2nd rowA2
3rd rowABC
4th rowABC
5th rowAAA

Common Values

ValueCountFrequency (%)
BB 1946
 
6.9%
AAA 1939
 
6.9%
BCB 1933
 
6.9%
B2 1931
 
6.8%
A 1930
 
6.8%
D 1922
 
6.8%
B 1882
 
6.7%
AB 1868
 
6.6%
ABC 1867
 
6.6%
A2 1855
 
6.6%
Other values (4) 7368
26.1%

Length

2024-02-20T23:44:09.093258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bb 1946
 
7.4%
aaa 1939
 
7.3%
bcb 1933
 
7.3%
b2 1931
 
7.3%
a 1930
 
7.3%
d 1922
 
7.3%
b 1882
 
7.1%
ab 1868
 
7.1%
abc 1867
 
7.1%
a2 1855
 
7.0%
Other values (4) 7368
27.9%

Most occurring characters

ValueCountFrequency (%)
B 24466
44.7%
A 17041
31.1%
C 5650
 
10.3%
2 3786
 
6.9%
D 3765
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 50922
93.1%
Decimal Number 3786
 
6.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 24466
48.0%
A 17041
33.5%
C 5650
 
11.1%
D 3765
 
7.4%
Decimal Number
ValueCountFrequency (%)
2 3786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50922
93.1%
Common 3786
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 24466
48.0%
A 17041
33.5%
C 5650
 
11.1%
D 3765
 
7.4%
Common
ValueCountFrequency (%)
2 3786
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 24466
44.7%
A 17041
31.1%
C 5650
 
10.3%
2 3786
 
6.9%
D 3765
 
6.9%

cloud_level
Categorical

Distinct3
Distinct (%)< 0.1%
Missing276
Missing (%)1.0%
Memory size1.7 MiB
Low
13921 
Medium
13704 
Extremely Low
 
299

Length

Max length13
Median length6
Mean length4.5793583
Min length3

Characters and Unicode

Total characters127874
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowLow

Common Values

ValueCountFrequency (%)
Low 13921
49.4%
Medium 13704
48.6%
Extremely Low 299
 
1.1%
(Missing) 276
 
1.0%

Length

2024-02-20T23:44:09.436287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:44:09.697787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
low 14220
50.4%
medium 13704
48.6%
extremely 299
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 14302
11.2%
L 14220
11.1%
o 14220
11.1%
w 14220
11.1%
m 14003
11.0%
M 13704
10.7%
d 13704
10.7%
i 13704
10.7%
u 13704
10.7%
E 299
 
0.2%
Other values (6) 1794
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 99352
77.7%
Uppercase Letter 28223
 
22.1%
Space Separator 299
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14302
14.4%
o 14220
14.3%
w 14220
14.3%
m 14003
14.1%
d 13704
13.8%
i 13704
13.8%
u 13704
13.8%
x 299
 
0.3%
t 299
 
0.3%
r 299
 
0.3%
Other values (2) 598
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
L 14220
50.4%
M 13704
48.6%
E 299
 
1.1%
Space Separator
ValueCountFrequency (%)
299
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 127575
99.8%
Common 299
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14302
11.2%
L 14220
11.1%
o 14220
11.1%
w 14220
11.1%
m 14003
11.0%
M 13704
10.7%
d 13704
10.7%
i 13704
10.7%
u 13704
10.7%
E 299
 
0.2%
Other values (5) 1495
 
1.2%
Common
ValueCountFrequency (%)
299
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14302
11.2%
L 14220
11.1%
o 14220
11.1%
w 14220
11.1%
m 14003
11.0%
M 13704
10.7%
d 13704
10.7%
i 13704
10.7%
u 13704
10.7%
E 299
 
0.2%
Other values (6) 1794
 
1.4%

blade_length(m)
Real number (ℝ)

MISSING 

Distinct22833
Distinct (%)98.8%
Missing5093
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean2.2540343
Minimum-99
Maximum18.2098
Zeros0
Zeros (%)0.0%
Negative1441
Negative (%)5.1%
Memory size220.4 KiB
2024-02-20T23:44:09.941679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-0.58806721
Q12.5448587
median3.4533332
Q34.3578624
95-th percentile6.9377458
Maximum18.2098
Range117.2098
Interquartile range (IQR)1.8130038

Descriptive statistics

Standard deviation11.275602
Coefficient of variation (CV)5.0024093
Kurtosis74.420477
Mean2.2540343
Median Absolute Deviation (MAD)0.90638174
Skewness-8.6083575
Sum52083.97
Variance127.1392
MonotonicityNot monotonic
2024-02-20T23:44:10.266179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99 275
 
1.0%
2.217541643 1
 
< 0.1%
7.959351043 1
 
< 0.1%
2.643124626 1
 
< 0.1%
4.51922494 1
 
< 0.1%
4.808427809 1
 
< 0.1%
3.735344071 1
 
< 0.1%
4.408425717 1
 
< 0.1%
2.788587462 1
 
< 0.1%
0.01820307361 1
 
< 0.1%
Other values (22823) 22823
80.9%
(Missing) 5093
 
18.1%
ValueCountFrequency (%)
-99 275
1.0%
-8.253477801 1
 
< 0.1%
-7.624633073 1
 
< 0.1%
-7.538948621 1
 
< 0.1%
-7.536253236 1
 
< 0.1%
-7.219581046 1
 
< 0.1%
-7.192045258 1
 
< 0.1%
-7.100171412 1
 
< 0.1%
-7.091609311 1
 
< 0.1%
-6.944512936 1
 
< 0.1%
ValueCountFrequency (%)
18.20980014 1
< 0.1%
16.39279475 1
< 0.1%
15.8886668 1
< 0.1%
15.69148925 1
< 0.1%
15.21518982 1
< 0.1%
14.91905358 1
< 0.1%
13.91299359 1
< 0.1%
13.90801917 1
< 0.1%
13.88855651 1
< 0.1%
13.83555132 1
< 0.1%

blade_breadth(m)
Real number (ℝ)

UNIQUE 

Distinct28200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39724872
Minimum0.200111
Maximum0.49997527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size220.4 KiB
2024-02-20T23:44:10.577448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.200111
5-th percentile0.30576147
Q10.34744506
median0.39859103
Q30.4493545
95-th percentile0.48980627
Maximum0.49997527
Range0.29986427
Interquartile range (IQR)0.10190944

Descriptive statistics

Standard deviation0.061158329
Coefficient of variation (CV)0.15395476
Kurtosis-0.74096604
Mean0.39724872
Median Absolute Deviation (MAD)0.050993012
Skewness-0.19340132
Sum11202.414
Variance0.0037403412
MonotonicityNot monotonic
2024-02-20T23:44:10.870056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3140648354 1
 
< 0.1%
0.4208998661 1
 
< 0.1%
0.3710168093 1
 
< 0.1%
0.3343039712 1
 
< 0.1%
0.4143697904 1
 
< 0.1%
0.3394814137 1
 
< 0.1%
0.3824872436 1
 
< 0.1%
0.3719248239 1
 
< 0.1%
0.4968536746 1
 
< 0.1%
0.3115411452 1
 
< 0.1%
Other values (28190) 28190
> 99.9%
ValueCountFrequency (%)
0.2001109965 1
< 0.1%
0.2006787452 1
< 0.1%
0.2008081334 1
< 0.1%
0.2016219972 1
< 0.1%
0.2016267628 1
< 0.1%
0.2016990017 1
< 0.1%
0.2018582054 1
< 0.1%
0.2024768285 1
< 0.1%
0.2025600919 1
< 0.1%
0.2027508279 1
< 0.1%
ValueCountFrequency (%)
0.4999752685 1
< 0.1%
0.4999563119 1
< 0.1%
0.4999553959 1
< 0.1%
0.4999552337 1
< 0.1%
0.4999437506 1
< 0.1%
0.4999414096 1
< 0.1%
0.4999286256 1
< 0.1%
0.4999093262 1
< 0.1%
0.4999089142 1
< 0.1%
0.499892128 1
< 0.1%

windmill_height(m)
Real number (ℝ)

MISSING 

Distinct27657
Distinct (%)100.0%
Missing543
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean25.887052
Minimum-30.295253
Maximum78.351335
Zeros0
Zeros (%)0.0%
Negative149
Negative (%)0.5%
Memory size220.4 KiB
2024-02-20T23:44:11.176389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-30.295253
5-th percentile11.582362
Q124.447658
median25.957739
Q327.477854
95-th percentile40.357383
Maximum78.351335
Range108.64659
Interquartile range (IQR)3.0301965

Descriptive statistics

Standard deviation7.773609
Coefficient of variation (CV)0.30028946
Kurtosis4.514327
Mean25.887052
Median Absolute Deviation (MAD)1.5145424
Skewness-0.11328013
Sum715958.2
Variance60.428997
MonotonicityNot monotonic
2024-02-20T23:44:11.474785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.28168894 1
 
< 0.1%
20.53081879 1
 
< 0.1%
43.19790388 1
 
< 0.1%
27.17600551 1
 
< 0.1%
30.11345374 1
 
< 0.1%
24.19968174 1
 
< 0.1%
27.75915062 1
 
< 0.1%
27.47492239 1
 
< 0.1%
27.36590975 1
 
< 0.1%
43.24089213 1
 
< 0.1%
Other values (27647) 27647
98.0%
(Missing) 543
 
1.9%
ValueCountFrequency (%)
-30.29525292 1
< 0.1%
-20.91863575 1
< 0.1%
-19.33419269 1
< 0.1%
-18.4397337 1
< 0.1%
-18.21645645 1
< 0.1%
-17.48041945 1
< 0.1%
-17.10268626 1
< 0.1%
-17.03202204 1
< 0.1%
-16.3026199 1
< 0.1%
-16.21053862 1
< 0.1%
ValueCountFrequency (%)
78.35133528 1
< 0.1%
71.00520433 1
< 0.1%
69.92471772 1
< 0.1%
69.87290299 1
< 0.1%
68.95570753 1
< 0.1%
67.69536211 1
< 0.1%
67.04473569 1
< 0.1%
66.0622979 1
< 0.1%
65.71912599 1
< 0.1%
65.23024386 1
< 0.1%

windmill_generated_power(kW/h)
Real number (ℝ)

HIGH CORRELATION 

Distinct27988
Distinct (%)> 99.9%
Missing207
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean6.1305293
Minimum0.96230495
Maximum20.175358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size220.4 KiB
2024-02-20T23:44:11.785617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.96230495
5-th percentile2.4091179
Q14.0595054
median5.7647103
Q37.9471946
95-th percentile10.91448
Maximum20.175358
Range19.213053
Interquartile range (IQR)3.8876891

Descriptive statistics

Standard deviation2.6975204
Coefficient of variation (CV)0.44001427
Kurtosis0.43704314
Mean6.1305293
Median Absolute Deviation (MAD)1.9139208
Skewness0.6889353
Sum171611.91
Variance7.2766161
MonotonicityNot monotonic
2024-02-20T23:44:12.090721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3616999 2
 
< 0.1%
7.6718 2
 
< 0.1%
4.5167 2
 
< 0.1%
3.088373537 2
 
< 0.1%
2.8142 2
 
< 0.1%
7.24868954 1
 
< 0.1%
9.878389175 1
 
< 0.1%
12.70199955 1
 
< 0.1%
2.982464895 1
 
< 0.1%
7.931238556 1
 
< 0.1%
Other values (27978) 27978
99.2%
(Missing) 207
 
0.7%
ValueCountFrequency (%)
0.9623049455 1
< 0.1%
0.9802399545 1
< 0.1%
1.000244378 1
< 0.1%
1.00773494 1
< 0.1%
1.028254936 1
< 0.1%
1.030446603 1
< 0.1%
1.045973629 1
< 0.1%
1.050784135 1
< 0.1%
1.063274913 1
< 0.1%
1.066878877 1
< 0.1%
ValueCountFrequency (%)
20.17535792 1
< 0.1%
20.12793935 1
< 0.1%
19.9989395 1
< 0.1%
19.94138874 1
< 0.1%
19.31291405 1
< 0.1%
19.0634092 1
< 0.1%
18.98303925 1
< 0.1%
18.69422425 1
< 0.1%
18.6151785 1
< 0.1%
18.0758194 1
< 0.1%

Interactions

2024-02-20T23:43:50.488413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:21.969043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:27.663622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:32.566242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:37.745286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:42.712529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:47.615859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:53.102366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:58.069438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:03.498598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:08.410475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:13.717587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:19.665442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:24.256149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:29.648513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:34.681849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:39.896187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:45.475859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:50.880257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:22.221405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:27.911986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:32.827729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:38.113966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:42.957144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:47.855377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:53.356391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:58.322026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:03.814400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:08.642331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:13.977540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:19.918658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:24.502979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:30.061315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:34.924168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:40.171055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:45.729252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:51.311821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:22.488735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:28.172156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:33.103424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:38.517667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:43.236016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:48.114661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:53.629054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:58.582340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:04.237605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:08.905015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:14.253036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:20.191539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:24.793502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:30.443100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:35.214975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:40.476126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:45.984786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:51.751544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:22.727194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:28.422116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:33.339512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:38.843950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:43.482836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:48.364404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:53.880242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:58.850357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:04.605079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:09.168708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:14.514865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:20.435456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:25.049707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:30.703821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:35.464007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:40.801006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:46.237344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:52.104222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:23.055418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:28.697492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:33.582943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:39.109468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:44.008123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:48.606449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:54.138916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:59.126337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:04.864329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:09.414453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:14.857570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:20.709707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:25.311821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:30.966912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:35.717234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:41.133833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:46.495483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:52.367328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:23.630497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:28.957945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:33.849201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:39.374311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:44.275677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:48.921223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:54.393674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:59.381482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:05.137966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:09.652826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:15.268744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:20.955899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:25.564150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:31.251983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:35.979609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:41.520242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:46.756040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:52.614984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:24.000058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:29.209835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:34.104594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:39.620627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:44.521846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:49.273242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:54.651184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:59.619992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:05.372896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:09.887374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:15.611443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:21.206262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:25.823277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:31.506034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:36.243978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:41.863714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:46.996981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:52.881332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:24.375409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:29.465333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:34.353301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:39.874890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-20T23:42:29.993312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:34.867233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:40.401945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:45.307924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:50.445358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:55.418804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:00.403899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-20T23:43:21.972832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:26.587601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-20T23:43:00.634514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-20T23:43:11.059078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:17.562906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:22.203146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:26.833910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:32.539011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:37.263669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-20T23:43:43.652494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-20T23:43:54.551366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:26.117667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:30.752452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:35.591969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:41.158104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:46.048271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:51.503517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:56.178094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:01.164928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:06.872123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:11.861495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:18.085294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:22.702665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:27.382811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:33.072858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:38.309508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:43.902853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:48.505000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:54.949430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:26.361542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:31.006499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:35.847414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:41.400886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-20T23:43:12.119288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:18.340193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:22.950711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:27.733515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:33.331649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:38.560942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:44.158190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:48.770372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:55.371716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:26.653076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:31.277642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:36.201819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:41.658709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:46.570811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:52.072452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:56.697722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:01.700424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:07.392541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:12.680422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:18.612340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:23.217000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:28.058512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:33.598848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-20T23:43:49.051536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:55.785121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:26.903964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:31.528652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:36.588303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:41.915002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:46.828158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:52.321859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:56.954770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:02.058393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:07.643399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:12.933672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:18.877203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:23.476269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:28.450963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:33.861431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:39.095721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:44.693569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:49.417062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:56.195905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:27.157859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:31.810659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:36.976425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:42.195234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:47.095199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:52.597134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:57.215466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:02.454107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:07.897889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:13.195959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:19.145105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:23.738573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:28.838000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:34.154073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:39.373981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:44.956448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:49.791484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:56.531018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:27.392350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:32.070172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:37.356936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:42.445389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:47.359220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:52.841821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:42:57.462070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:03.103611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:08.147408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:13.456306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:19.395975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:23.990986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:29.226973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:34.411843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:39.626958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:45.212569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-20T23:43:50.187595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-20T23:44:12.378316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
area_temperature(°C)atmospheric_pressure(Pascal)atmospheric_temperature(°C)blade_breadth(m)blade_length(m)blades_angle(°)cloud_levelengine_temperature(°C)gearbox_temperature(°C)generator_temperature(°C)motor_torque(N-m)resistance(ohm)rotor_torque(N-m)shaft_temperature(°C)turbine_statuswind_direction(°)wind_speed(m/s)windmill_body_temperature(°C)windmill_generated_power(kW/h)windmill_height(m)
area_temperature(°C)1.000-0.0490.3230.0180.0150.0450.0240.4120.2550.5900.5320.4120.3700.3660.0000.1850.4390.3770.392-0.001
atmospheric_pressure(Pascal)-0.0491.000-0.2490.020-0.001-0.0720.0060.0760.066-0.240-0.252-0.199-0.1810.0300.0000.063-0.263-0.0920.2320.017
atmospheric_temperature(°C)0.323-0.2491.0000.0020.0040.0730.0180.1350.0510.4650.4490.3550.3160.1070.0000.0260.3890.2320.0050.000
blade_breadth(m)0.0180.0200.0021.0000.039-0.0390.4720.0300.0300.0250.0300.0190.0260.0100.0040.0200.009-0.0020.0620.033
blade_length(m)0.015-0.0010.0040.0391.000-0.0290.0030.0210.0200.0250.0290.0290.0140.0080.0000.0080.0170.0010.0450.013
blades_angle(°)0.045-0.0720.073-0.039-0.0291.0000.243-0.080-0.100-0.028-0.052-0.047-0.034-0.0530.0000.0510.0580.088-0.208-0.006
cloud_level0.0240.0060.0180.4720.0030.2431.0000.0210.0190.0150.0180.0140.0220.0010.0120.008-0.005-0.0150.0520.022
engine_temperature(°C)0.4120.0760.1350.0300.021-0.0800.0211.0000.3050.4730.4530.3650.3130.2980.0000.2120.1930.1480.5600.012
gearbox_temperature(°C)0.2550.0660.0510.0300.020-0.1000.0190.3051.0000.2800.2730.2160.1930.1930.0000.1180.1010.0840.3820.014
generator_temperature(°C)0.590-0.2400.4650.0250.025-0.0280.0150.4730.2801.0000.9700.7740.6830.3040.0110.2000.5630.2780.5350.002
motor_torque(N-m)0.532-0.2520.4490.0300.029-0.0520.0180.4530.2730.9701.0000.7850.6970.2850.0090.1850.5420.2470.5540.004
resistance(ohm)0.412-0.1990.3550.0190.029-0.0470.0140.3650.2160.7740.7851.0000.5510.2140.0040.1520.4290.1950.4480.002
rotor_torque(N-m)0.370-0.1810.3160.0260.014-0.0340.0220.3130.1930.6830.6970.5511.0000.2040.0120.1250.3810.1740.3880.016
shaft_temperature(°C)0.3660.0300.1070.0100.008-0.0530.0010.2980.1930.3040.2850.2140.2041.0000.0000.1060.1650.1400.3200.001
turbine_status0.0000.0000.0000.0040.0000.0000.0120.0000.0000.0110.0090.0040.0120.0001.000-0.0040.009-0.005-0.0130.014
wind_direction(°)0.1850.0630.0260.0200.0080.0510.0080.2120.1180.2000.1850.1520.1250.106-0.0041.0000.0370.0180.2730.013
wind_speed(m/s)0.439-0.2630.3890.0090.0170.058-0.0050.1930.1010.5630.5420.4290.3810.1650.0090.0371.0000.3280.119-0.012
windmill_body_temperature(°C)0.377-0.0920.232-0.0020.0010.088-0.0150.1480.0840.2780.2470.1950.1740.140-0.0050.0180.3281.0000.071-0.006
windmill_generated_power(kW/h)0.3920.2320.0050.0620.045-0.2080.0520.5600.3820.5350.5540.4480.3880.320-0.0130.2730.1190.0711.0000.017
windmill_height(m)-0.0010.0170.0000.0330.013-0.0060.0220.0120.0140.0020.0040.0020.0160.0010.0140.013-0.012-0.0060.0171.000

Missing values

2024-02-20T23:43:56.952516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-20T23:43:57.658787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-20T23:43:58.450423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

wind_speed(m/s)atmospheric_temperature(°C)shaft_temperature(°C)blades_angle(°)gearbox_temperature(°C)engine_temperature(°C)motor_torque(N-m)generator_temperature(°C)atmospheric_pressure(Pascal)area_temperature(°C)windmill_body_temperature(°C)wind_direction(°)resistance(ohm)rotor_torque(N-m)turbine_statuscloud_levelblade_length(m)blade_breadth(m)windmill_height(m)windmill_generated_power(kW/h)
094.820023-99.00000041.723019-0.90342382.41057342.5230152563.12452276.665560103402.96187226.897875NaN239.8363882730.31060542.084666BAMedium2.2175420.31406524.2816896.766521
1241.83273427.764785-99.000000-99.00000044.10491946.2588702372.38411978.12980317030.90407839.801469NaN337.9447231780.207200107.888643A2Medium4.2103460.44849427.2621395.966275
295.484724NaN41.85547312.65276342.32209842.8785521657.16964667.65446916125.92710736.11606545.033197227.8502941666.049900-42.931459ABCMedium2.7194750.30232127.3661272.874342
3238.819424-99.00000045.44391415.11532344.75964347.2821012888.13407995.38997418689.73233646.02004544.827154492.0815201964.50289542.744596ABCNaN4.8573850.36714024.28776714.851089
410.722890NaN41.9811831.715696-17.61645943.469852781.69541937.423065114468.16900734.572941-99.000000259.2746011177.51615213.387289AAAMediumNaN0.45337427.9716503.519074
593.76997330.32622617.970619-99.00000043.81643040.8157952119.35165372.345126NaN35.315292101.378184NaN1715.24412197.746463ABCLow2.5040980.39564224.6732924.945780
616.026249-99.00000044.072819-0.19684541.68058343.384904778.10998540.284018121813.38315833.84938943.008746528.0039851222.93127011.805113BDLow2.9179220.44734133.5935115.089173
748.73782612.71681543.217778-99.000000-48.40508944.125843980.98853143.691867120923.02489330.553159-99.000000NaN1177.63734118.384873BALow2.9388100.35488129.9448218.536889
847.081729-99.000000-33.607048-99.00000043.05542745.253628957.58015141.609787119628.96470926.16807343.216062281.368625-99.00000019.486763ABCLow1.6514380.30155046.7335098.739166
9283.78932918.88793241.69146952.337026-62.72436241.8812561042.08613565.28022516160.28160529.381862-99.000000352.2685211662.07627720.100683ACExtremely Low1.0608170.20169924.3240681.948810
wind_speed(m/s)atmospheric_temperature(°C)shaft_temperature(°C)blades_angle(°)gearbox_temperature(°C)engine_temperature(°C)motor_torque(N-m)generator_temperature(°C)atmospheric_pressure(Pascal)area_temperature(°C)windmill_body_temperature(°C)wind_direction(°)resistance(ohm)rotor_torque(N-m)turbine_statuscloud_levelblade_length(m)blade_breadth(m)windmill_height(m)windmill_generated_power(kW/h)
28190-139.7501623.90005119.267247-1.24341513.63884015.799740796.84687439.072592118873.55823924.426160NaNNaN1265.29343214.161226AAALow4.3602370.48143717.5410746.195231
2819196.297920-99.00000048.0972787.67618946.83799445.2150642800.77638597.85228319156.69304941.470538-99.000000513.108780893.85171347.959070B2MediumNaN0.3151512.34953210.390029
2819220.1771017.00763943.902664-99.00000043.22130745.315933845.39756140.166194120708.87455031.26106878.755524273.8570271272.38245215.568416BBLow2.2659010.47744725.3673256.448082
2819395.03303128.05413845.408044-1.14976145.56734543.2237202389.29928675.38126917735.58807333.78282244.536334247.4510391776.523603-99.000000ABMedium2.4181640.40019533.4627196.683816
2819413.0966949.93601942.09304772.51349439.91354043.276769757.16148338.284383116089.70263137.7851792.414511144.5817051196.73747813.377223BDMediumNaN0.35170724.2890084.186744
2819594.76569923.57679345.3993525.377222-1.08517148.5282482791.60099090.89887519428.72507945.42923044.242269536.1534131980.86192145.909054BBMedium2.7743350.41829924.5908019.587934
2819694.19673824.03432942.068979-99.00000044.28515343.4879392207.88227672.24464516596.48540025.14268143.616412354.2398251712.84045736.974913BBLow-3.2509890.46153126.0516044.522195
2819794.16046328.67429645.0042139.55035849.37770644.0426322801.65737494.81463719083.88144945.12944243.576510534.2099131951.72871388.319152DMediumNaN0.38026428.53385011.096599
2819895.43037726.56025448.0326243.05138981.44389644.8213652760.64728090.14441818360.78570745.60392744.973415568.5048981968.91769247.562627BCBLow3.0018550.34644747.7472699.373239
2819943.55835218.7212319.2202082.59363940.26227940.9019752015.97741069.043447-256507.54767222.41340744.100782320.4980701666.526546-8.932053BBBLowNaN0.38885524.1254082.860342